System and method for supplementing a question answering system with mixed-language source documents

ABSTRACT

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising receiving a question in a target language; determining the question cannot be answered using a target-language only corpus; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity; if the dependency is similar, returning the source language entity as a member of a set; populating the set of returned source language entities for each focus in the target language question; identifying one or more parallel passages wherein all core arguments are matched; for each parallel passage: identifying the presence or absence of oblique nominal arguments; and measuring the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and returning an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under contract number 2013-12101100008 awarded by United States defense agencies. The government has certain rights to this invention.

TECHNICAL FIELD

The present application relates generally to a system and method that can be used to supplement a question answering system with mixed-language source documents.

BACKGROUND

Mixed-language question answering systems sometimes lack sufficient information to properly answer a question due to data sparsity in the target language. However, the answer may be found in a corpus of a different language, which might be larger or otherwise better suited to the domain. What is needed is a method for both identifying and scoring candidate answers to questions using source documents from other languages.

SUMMARY

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising receiving a question in a target language; determining the question cannot be answered using a target-language only corpus; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity; if the dependency is similar, returning the source language entity as a member of a set; populating the set of returned source language entities for each focus in the target language question; identifying one or more parallel passages wherein all core arguments are matched; for each parallel passage: identifying the presence or absence of oblique nominal arguments; and measuring the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and returning an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.

Embodiments can further provide a method further comprising storing the one or more Abstract Universal Verbal Types in an ontology.

Embodiments can further provide a method further comprising receiving a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.

Embodiments can further provide a method further comprising generating the correspondence mapping through natural language analysis of a mixed-language corpus.

Embodiments can further provide a method further comprising assigning a function from the target language entities to the source language entities.

Embodiments can further provide a method further comprising assigning a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types.

Embodiments can further provide a method further comprising assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language.

Embodiments can further provide a method further comprising assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.

In another illustrative embodiment, a computer program product comprising a computer usable or readable medium having a computer readable program is provided. The computer readable program, when executed on a processor, causes the processor to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

In yet another illustrative embodiment, a system is provided. The system may comprise a mixed-language question answering supplement processor configured to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.

Additional features and advantages of this disclosure will be made apparent from the following detailed description of illustrative embodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are best understood from the following detailed description when read in connection with the accompanying drawings. For the purpose of illustrating the invention, there is shown in the drawings embodiments that are presently preferred, it being understood, however, that the invention is not limited to the specific instrumentalities disclosed. Included in the drawings are the following Figures:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of a cognitive system implementing a mixed-language question answering (QA) supplement system in a computer network;

FIG. 2 is a block diagram of an example data processing system in which aspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline, of a cognitive system, for processing an input question generated from the mixed-language QA supplement system in accordance with one illustrative embodiment; and

FIG. 4 depicts a block diagram illustrating the functionality of a mixed-language QA supplement system, according to embodiments described herein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The mixed-language question answering (QA) supplement system can work by mapping the named entities in the question (in the “target” language) to named entities in the passage (in the “source” language), and then mapping the target language verbs that are connected to source language verbs and other verbs in their hypernym/hyponym chain. The system can then identify candidate answers that are connected to the source language verb in the same manner as the target language verb is to its focus.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a head disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network (LAN), a wide area network (WAN), and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object-oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including LAN or WAN, or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operations steps to be performed on the computer, other programmable apparatus, or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

As an overview, a cognitive system is a specialized computer system, or set of computer systems, configured with hardware and/or software logic (in combination with hardware logic upon which the software executes) to emulate human cognitive functions. These cognitive systems apply human-like characteristics to conveying and manipulating ideas which, when combined with the inherent strengths of digital computing, can solve problems with high accuracy and resilience on a large scale. IBM Watson™ is an example of one such cognitive system which can process human readable language and identify inferences between text passages with human-like accuracy at speeds far faster than human beings and on a much larger scale. In general, such cognitive systems are able to perform the following functions:

-   -   Navigate the complexities of human language and understanding     -   Ingest and process vast amounts of structured and unstructured         data     -   Generate and evaluate hypotheses     -   Weigh and evaluate responses that are based only on relevant         evidence     -   Provide situation-specific advice, insights, and guidance     -   Improve knowledge and learn with each iteration and interaction         through machine learning processes     -   Enable decision making at the point of impact (contextual         guidance)     -   Scale in proportion to the task     -   Extend and magnify human expertise and cognition     -   Identify resonating, human-like attributes and traits from         natural language     -   Deduce various language specific or agnostic attributes from         natural language     -   High degree of relevant recollection from data points (images,         text, voice) (memorization and recall)     -   Predict and sense with situation awareness that mimic human         cognition based on experiences     -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answering questions posed to these cognitive systems using a Question Answering pipeline or system (QA system). The QA pipeline or system is an artificial intelligence application executing on data processing hardware that answers questions pertaining to a given subject-matter domain presented in natural language. The QA pipeline receives inputs from various sources including input over a network, a corpus of electronic documents or other data, data from a content creator, information from one or more content users, and other such inputs from other possible sources of input, which can be in multiple languages. Data storage devices store the corpus of data. A content creator creates content in a document for use as part of a corpus of data with the QA pipeline. The document may include any file, text, article, or source of data for use in the QA system. For example, a QA pipeline accesses a body of knowledge about the domain, or subject matter area (e.g., financial domain, medical domain, legal domain, etc.) where the body of knowledge (knowledgebase) can be organized in a variety of configurations, e.g., a structured repository of domain-specific information, such as ontologies, or unstructured data related to the domain, or a collection of natural language documents about the domain.

Content users can input questions in a particular language to the cognitive system which implements the QA pipeline. The QA pipeline then answers the input questions using the content in the corpus or data by evaluating documents, sections of documents, portions of data in the corpus, or the like. When a process evaluates a given section of a document for semantic content, the process can use a variety of conventions to query such document from the QA pipeline, e.g., sending the query to the QA pipeline as a well-formed question which is then interpreted by the QA pipeline and a response is provided containing one or more answers to the question. Semantic content is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic content is content that interprets an expression, such as by using natural language processing.

As will be described in greater detail hereafter, the QA pipeline receives an input question, parses the question to extract the major features of the question, uses the extracted features to formulate queries, and then applies those queries to the corpus of data. Based on the application of the queries to the corpus of data, the QA pipeline generates a set of hypotheses, or candidate answers to the input question, by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The QA pipeline then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. There may be hundreds or even thousands of reasoning algorithms applied, each of which performs different analysis, e.g., comparisons, natural language analysis, lexical analysis, or the like, and generates a score. For example, some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model. The statistical model captures how well the reasoning algorithm performed at establishing the inference between two similar passages for a particular domain during the training period of the QA pipeline. The statistical model is used to summarize a level of confidence that the QA pipeline has regarding the evidence that the potential response, i.e., candidate answer, is inferred by the question. This process is repeated for each of the candidate answers until the QA pipeline identifies candidate answers that surface as being significantly stronger than others and thus generates a final answer, or ranked set of answers, for the input question.

As mentioned above, QA pipeline and mechanisms operate by accessing information from a corpus of data or information (also referred to as a corpus of content), analyzing it, and then generating answer results based on the analysis of this data. Accessing information from a corpus of data typically includes: a database query that answers questions about what is in a collection of structured records, and a search that delivers a collection of document links in response to a query against a collection of unstructured data (text, markup language, etc.). Conventional question answering systems are capable of generating answers based on the corpus of data and the input question, verifying answers to a collection of questions for the corpus of data, correcting errors in digital text using a corpus of data, and selecting answers to questions from a pool of potential answers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators, web page authors, document database creators, and the like, determine use cases for products, solutions, and services described in such content before writing their content. Consequently, the content creators know what questions the content is intended to answer in a particular topic addressed by the content. Categorizing the questions, such as in terms of roles, type of information, tasks, or the like, associated with the question, in each document of a corpus of data allows the QA pipeline to more quickly and efficiently identify documents containing content related to a specific query. The content may also answer other questions that the content creator did not contemplate that may be useful to content users. The questions and answers may be verified by the content creator to be contained in the content for a given document. These capabilities contribute to improved accuracy, system performance, machine learning, and confidence of the QA pipeline. Content creators, automated tools, or the like, annotate or otherwise generate metadata for providing information useable by the QA pipeline to identify question and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for input questions using a plurality of intensive analysis mechanisms which evaluate the content to identify the most probable answers, i.e., candidate answers, for the input question. The most probable answers are output as a ranked listing of candidate answers ranked according to their relative scores or confidence measures calculated during evaluation of the candidate answers, as a single final answer having a highest ranking score or confidence measure, or which is a best match to the input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of a cognitive system 100 implementing a question and answer (QA) pipeline 108 in a computer network 102. One example of a question/answer generation operation which may be used in conjunction with the principles described herein is described in U.S. Patent Application Publication No. 2011/0125734, which is herein incorporated by reference in its entirety. The cognitive system 100 is implemented on one or more computing devices 104 (comprising one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like) connected to the computer network 102. The network 102 includes multiple computing devices 104 in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link comprises one or more of wires, routers, switches, transmitters, receivers, or the like. The cognitive system 100 and network 102 enables question/answer (QA) generation functionality for one or more cognitive system users via their respective computing devices. Other embodiments of the cognitive system 100 may be used with components, systems, sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108 that receive inputs from various sources. For example, the cognitive system 100 receives input from the network 102, corpora of electronic documents of multiple languages 140, cognitive system users, and/or other data and other possible sources of input. In one embodiment, some or all of the inputs to the cognitive system 100 are routed through the network 102. The various computing devices 104 on the network 102 include access points for content creators and QA system users. Some of the computing devices 104 include devices for a database storing the corpora of data 140. Portions of the corpora of data 140 may also be provided on one or more other network attached storage devices, in one or more databases, or other computing devices not explicitly shown in FIG. 1. The network 102 includes local network connections and remote connections in various embodiments, such that the cognitive system 100 may operate in environments of any size, including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document of the corpus of data 140 for use as part of a corpus of data with the cognitive system 100. The document includes any file, text, article, or source of data for use in the cognitive system 100. Mixed-language QA supplement system users access the cognitive system 100 via a network connection or an Internet connection to the network 102, and input questions to the cognitive system 100 that are answered by the content in the corpus of data 140. In one embodiment, the questions are formed using natural language. The cognitive system 100 parses and interprets the question via a QA pipeline 108, and provides a response to the cognitive system user containing one or more answers to the question. In some embodiments, the cognitive system 100 provides a response to users in a ranked list of candidate answers while in other illustrative embodiments, the cognitive system 100 provides a single final answer or a combination of a final answer and ranked listing of other candidate answers.

The cognitive system 100 implements the QA pipeline 108 which comprises a plurality of stages for processing an input question and the corpus of data 140. The QA pipeline 108 generates answers for the input question based on the processing of the input question and the corpus of data 140. The QA pipeline 108 will be described in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be the IBM Watson™ cognitive system available from International Business Machines Corporation of Armonk, New York, which is augmented with the mechanisms of the illustrative embodiments described hereafter. As outlined previously, a QA pipeline of the IBM Watson™ cognitive system receives an input question, which it then parses to extract the major features of the question, and which in turn are then used to formulate queries that are applied to the corpus of data. Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question. The QA pipeline of the IBM Watson™ cognitive system then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. The scores obtained from the various reasoning algorithms are then weighted against a statistical model that summarizes a level of confidence that the QA pipeline of the IBM Watson™ cognitive system has regarding the evidence that the potential response, i.e., candidate answer, is inferred by the question. This process is repeated for each of the candidate answers to generate ranked listing of candidate answers which may then be presented to the user that submitted the input question, or from which a final answer is selected and presented to the user. More information about the QA pipeline of the IBM Watson™ cognitive system may be obtained, for example, from the IBM Corporation website, IBM Redbooks, and the like. For example, information about the QA pipeline of the IBM Watson™ cognitive system can be found in Yuan et al., “Watson and Healthcare.” IBM developerWorks, 2011 and “The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012.

As shown in FIG. 1, in accordance with some illustrative embodiments, the cognitive system 100 is further augmented, in accordance with the mechanisms of the illustrative embodiments, to include logic implemented in specialized hardware, software executed on hardware, or any combination of specialized hardware and software executed on hardware, for integrating a mixed-language question answering (QA) supplement system 120.

The mixed-language QA supplement system 120 can include a correspondence mapping module 121, which can contain a mapping of named entities in a target language to corresponding named entities in a source language. For example, a correspondence mapping can be the “same-as” links in Wikipedia, which can connect equivalent pages in different languages. The correspondence mapping can be generated through a natural language analysis of documents contained in a mixed-language corpus. Additionally, the mixed-language QA supplement system 120 can include an ontology of Abstract Universal Verbal Types (AUVT) 123, which can contain lexical manifestations in both the source and the target example. Abstract Universal Verbal Types can be verbal mechanisms that cross language barriers, such that the verb is not changed in meaning, but merely in the language itself. Hypernym/hyponym relationships can be stored in the ontology 123. A hyponym is a word or phrase whose semantic field is included within that of another word, which is its hypernym. For example, color is a hypernym of purple, red, blue, and green. Purple is a hypernym of crimson, violet, and lavender.

One or more questions in a target language received from a user can be subjected to natural language processing techniques of the cognitive system 100 and/or mixed-language QA supplement system 120 to transform the questions into acyclic graphs where nodes represent potential facts, and connectors represent the overall connections between the potential facts. This operation may be performed, for example, as part of an ingestion operation of the cognitive system 100 which reads the natural language text of the electronic documents or asked questions, parses the natural language text and performs natural language processing on the natural language text, including performing annotation operations using annotators, to extract key features and facts of the natural language text which are then converted to the acyclic graphs. The reading, parsing, and extracting can occur independent of the language of the electronic documents or asked questions.

The acyclic graphs of the analyzed QA pairs are stored in storage device 150 associated with either the cognitive system 100 or the mixed-language QA supplement system 120, where the storage device 150 may be a memory, a hard disk based storage device, flash memory, solid state storage device, or the like (hereafter assumed to be a “memory” with in-memory representations of the acyclic graphs for purposes of description). The in-memory acyclic graphs are then analyzed by the reusable branch engine 122 of the mixed-language QA supplement system 120 to identify reusable branches within the acyclic graphs and a reusable branch data structure having entries for each reusable branch found in this way, and other reusable branches either found in other corpora, readily known and pre-populated in the reusable branch data structure by subject matter experts, or the like, is generated, as described further in FIG. 4. The identification of the reusable branches may further be associated with the in-memory acyclic graph of the corresponding question as well so as to identify for the particular knowledge domain what the reusable branches are in the knowledge domain.

Either as part of an ingestion operation, or by the QA acyclic graph analysis engine 124 analyzing the acyclic graphs generated by the ingestion operation, a QA element data structure 126 defining the various knowledge domains in the ingested corpus 140, as well as other QA elements pre-populated in the QA element data structure 126 either through analysis of other corpora or through manual input by subject matter expert interaction, is generated.

The QA element data structure 126 is analyzed by clustering engine 128 to identify clusters of QA elements based on their characteristics. For example, QA elements may be clustered according to similar QA element types to form QA element clusters. These clusters may be stored in a cluster data structure 129. As noted above, some QA elements may have sub-elements and various levels of clustering may be performed, which may be classified/clustered into other clusters. Thus, the same QA element may be present in multiple clusters.

In response to receiving the input utterance, a similar QA element search engine 130 performs a search of the cluster data structure 129 to generate a listing of the QA element clusters that involve the given new QA element(s). In making this list, the cluster search engine 130 may analyze the clusters of reusable branches that contain the new QA element to produce an initial list of candidate QA elements. This listing is then extended with candidate QA elements for similar QA elements obtained from clusters with which the elements of the reusable branches involving the new QA element are clustered.

Alternatively, the clustering performed by the clustering engine 128 may be performed after the identification of similar QA elements to those of the reusable branches found as having the new QA element(s), performed by the similar QA element search engine 130 and the list may then be extended with candidate QA elements for similar QA elements by using a provided QA ontology data structure 132. Those candidate elements may be included in the listing and the listing may be analyzed by the clustering engine 128 to generate clusters of QA elements for storage in the cluster data structure 129.

In either case, the similar QA element search engine 130 then determines whether the presented question already contains any of the clusters of candidate QA elements, i.e., the clusters identified as having the new QA element(s) in the request. For those that are already present within the utterance, the candidate clusters may be promoted in the listing to generate a filtered listing of candidate QA elements and their clusters 134.

The QA element clusters in the filtered listing of candidate QA elements 134 are then analyzed by a QA element compatibility engine 136 to identify which of the element clusters are compatible with the knowledge domain of the question that is to be answered. The QA element compatibility engine 136 may utilize configured association rules learned during a training of the mixed-language QA supplement system 120 and knowledge base, where the association rules specify compatibility of QA elements with different knowledge domains. Using these association rules, the QA element compatibility engine determines what combinations or patterns of one or more QA elements are found in questions asked by subject matter experts working in the same knowledge domain. The intersection of the association rules with the candidate QA element clusters indicates which element clusters are compatible with the knowledge domain. The resulting candidate clusters that intersect with the association rules may then be ranked by the QA element compatibility engine 136, such as based on frequency of appearance of the clusters or QA elements in the clusters. Other ranking criteria may also be utilized as noted above.

A QA element cluster in the filtered listing of candidate clusters 134, which also intersects with one or more of the association rules, is selected by the QA element compatibility engine 136 for use in providing an answer to a question. This selection may be based on the ranking of the clusters intersecting the association rules as discussed above. For example, a top ranked cluster may be selected for use in presenting an answer to the utterance. Alternatively, other selection criteria may be utilized as well, such as in an implementation where ranking of the clusters may not be performed, as previously discussed above.

FIG. 2 is a block diagram of an example data processing system 200 in which aspects of the illustrative embodiments are implemented. Data processing system 200 is an example of a computer, such as a server or client, in which computer usable code or instructions implementing the process for illustrative embodiments of the present invention are located. In one embodiment, FIG. 2 represents a server computing device, such as a server, which implements the mixed-language QA supplement system 120 and cognitive system 100 described herein.

In the depicted example, data processing system 200 can employ a hub architecture including a north bridge and memory controller hub (NB/MCH) 201 and south bridge and input/output (I/O) controller hub (SB/ICH) 202. Processing unit 203, main memory 204, and graphics processor 205 can be connected to the NB/MCH 201. Graphics processor 205 can be connected to the NB/MCH through an accelerated graphics port (AGP).

In the depicted example, the network adapter 206 connects to the SB/ICH 202. The audio adapter 207, keyboard and mouse adapter 208, modem 209, read only memory (ROM) 210, hard disk drive (HDD) 211, optical drive (CD or DVD) 212, universal serial bus (USB) ports and other communication ports 213, and the PCI/PCIe devices 214 can connect to the SB/ICH 202 through bus system 216. PCI/PCIe devices 214 may include Ethernet adapters, add-in cards, and PC cards for notebook computers. ROM 210 may be, for example, a flash basic input/output system (BIOS). The HDD 211 and optical drive 212 can use an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. The super I/O (SIO) device 215 can be connected to the SB/ICH.

An operating system can run on processing unit 203. The operating system can coordinate and provide control of various components within the data processing system 200. As a client, the operating system can be a commercially available operating system. An object-oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provide calls to the operating system from the object-oriented programs or applications executing on the data processing system 200. As a server, the data processing system 200 can be an IBM® eServer™ System p® running the Advanced Interactive Executive operating system or the Linux operating system. The data processing system 200 can be a symmetric multiprocessor (SMP) system that can include a plurality of processors in the processing unit 203. Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as the HDD 211, and are loaded into the main memory 204 for execution by the processing unit 203. The processes for embodiments of the mixed-language QA supplement system can be performed by the processing unit 203 using computer usable program code, which can be located in a memory such as, for example, main memory 204, ROM 210, or in one or more peripheral devices.

A bus system 216 can be comprised of one or more busses. The bus system 216 can be implemented using any type of communication fabric or architecture that can provide for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit such as the modem 209 or network adapter 206 can include one or more devices that can be used to transmit and receive data.

Those of ordinary skill in the art will appreciate that the hardware depicted in FIG. 2 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives may be used in addition to or in place of the hardware depicted. Moreover, the data processing system 200 can take the form of any of a number of different data processing systems, including but not limited to, client computing devices, server computing devices, tablet computers, laptop computers, telephone or other communication devices, personal digital assistants, and the like. Essentially, data processing system 200 can be any known or later developed data processing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline, of a cognitive system, for processing an input utterance in accordance with one illustrative embodiment. The QA system pipeline of FIG. 3 may be implemented, for example, as QA pipeline 108 of cognitive system 100 in FIG. 1. It should be appreciated that the stages of the QA pipeline shown in FIG. 3 are implemented as one or more software engines, components, or the like, which are configured with logic for implementing the functionality attributed to the particular stage. Each stage is implemented using one or more of such software engines, components, or the like. The software engines, components, etc., are executed on one or more processors of one or more data processing systems or devices and utilize or operate on data stored in one or more data storage devices, memories, or the like, on one or more of the data processing systems. The QA pipeline of FIG. 3 is augmented, for example, in one or more of the stages to implement the mixed-language QA supplement 120 mechanism of the illustrative embodiments described hereafter, additional stages may be provided to implement the improved mechanism, or separate logic from the pipeline 108 may be provided for interfacing with the pipeline 108 and implementing the improved functionality and operations of the illustrative embodiments.

As shown in FIG. 3, the QA pipeline 108 comprises a plurality of stages 310-380 through which the cognitive system operates to analyze an input question and generate a final response. In an initial question input stage 310, the QA pipeline 108 receives an input question that is presented in a natural language format. That is, a user inputs, via a user interface, an input question for which the user wishes to obtain an answer, e.g., “Who were Washington's closest advisors?” In response to receiving the input question, the next stage of the QA pipeline 108, i.e., the question and topic analysis stage 320, parses the input question using natural language processing (NLP) techniques to extract major features (also referred to as foci) from the input question, and classify the major features according to types, e.g., names, dates, or any of a plethora of other defined topics. For example, in the example question above, the term “who” may be associated with a topic for “persons” indicating that the identity of a person is being sought, “Washington” may be identified as a proper name of a person with which the question is associated, “closest” may be identified as a word indicative of proximity or relationship, and “advisors” may be indicative of a noun or other language topic.

In addition, the extracted major features include key words and phrases classified into question characteristics, such as the focus of the question, the lexical answer type (LAT) of the question, and the like. As referenced to herein, a lexical answer type (LAT) is a word in, or a word inferred from, the input question that indicates the type of the answer, independent of assigning semantics to that word. For example, in the question “What maneuver was invented in the 1500s to speed up the game and involves two pieces of the same color?” the LAT is the string “maneuver.” The focus of a question is the part of the question that, if replaced by the answer, makes the question a standalone statement. For example, in the question “What drug has been shown to relieve the symptoms of ADD with relatively few side effects?,” the focus is “drug” since if this word were replaced with the answer, e.g., “Adderall,” the answer can be used to replace the term “drug” to generate the sentence “Adderall has been shown to relieve the symptoms of ADD with relatively few side effects.” The focus often, but not always, contains the LAT. On the other hand, in many cases, it is not possible to infer a meaningful LAT from the focus.

Referring again to FIG. 3, the identified major features are then used during the question decomposition stage 330 to decompose the question into one or more queries that are applied to the mixed-language corpora of data/information 345 in order to generate one or more hypotheses. The queries are generated in any known or later developed query language, such as the Structure Query Language (SQL), or the like. The queries are applied to one or more databases storing information about the electronic texts, documents, articles, websites, and the like, that make up the mixed-language corpora of data/information 345. That is, these various sources themselves, different collections of sources, and the like, represent a different corpus 347 within the mixed-language corpora 345. There may be different corpora 345 defined for different collections of documents based on various criteria depending upon the particular implementation. For example, different corpora may be established for different topics, subject matter categories, sources of information, or the like. As one example, a first corpus may be associated with healthcare documents while a second corpus may be associated with financial documents. Alternatively, one corpus may be documents published by the U.S. Department of Energy while another corpus may be IBM Redbooks documents. Any collection of content having some similar attribute may be considered to be a corpus 347 within the mixed-language corpora 345.

The queries are applied to one or more databases storing information about the electronic texts, documents, articles, websites, and the like, that make up the corpus of data/information, e.g., the corpus of data 140 in FIG. 1. The queries are applied to the corpus of data/information at the hypothesis generation stage 340 to generate results identifying potential hypotheses for answering the input question, which can then be evaluated. That is, the application of the queries results in the extraction of portions of the corpus of data/information matching the criteria of the particular query. These portions of the corpus are then analyzed and used, during the hypothesis generation stage 340, to generate hypotheses for answering the input question. These hypotheses are also referred to herein as “candidate answers” for the input question. For any input question, at this stage 340, there may be hundreds of hypotheses or candidate answers generated that may need to be evaluated.

The QA pipeline 108, in stage 350, then performs a deep analysis and comparison of the language of the input question and the language of each hypothesis or “candidate answer,” as well as performs evidence scoring to evaluate the likelihood that the particular hypothesis is a correct answer for the input question. As described in FIG. 1, this involves using a plurality of reasoning algorithms, each performing a separate type of analysis of the language of the input question and/or content of the corpus that provides evidence in support of, or not in support of, the hypothesis. Each reasoning algorithm generates a score based on the analysis it performs which indicates a measure of relevance of the individual portions of the corpus of data/information extracted by application of the queries as well as a measure of the correctness of the corresponding hypothesis, i.e., a measure of confidence in the hypothesis. There are various ways of generating such scores depending upon the particular analysis being performed. In general, however, these algorithms look for particular terms, phrases, or patterns of text that are indicative of terms, phrases, or patterns of interest and determine a degree of matching with higher degrees of matching being given relatively higher scores than lower degrees of matching.

In the synthesis stage 360, the large number of scores generated by the various reasoning algorithms are synthesized into confidence scores or confidence measures for the various hypotheses. This process involves applying weights to the various scores, where the weights have been determined through training of the statistical model employed by the QA pipeline 108 and/or dynamically updated. For example, the weights for scores generated by algorithms that identify exactly matching terms and synonyms may be set relatively higher than other algorithms that are evaluating publication dates for evidence passages. The weights themselves may be specified by subject matter experts or learned through machine learning processes that evaluate the significance of characteristics evidence passages and their relative importance to overall candidate answer generation.

The weighted scores are processed in accordance with a statistical model generated through training of the QA pipeline 108 that identifies a manner by which these scores may be combined to generate a confidence score or measure for the individual hypotheses or candidate answers. This confidence score or measure summarizes the level of confidence that the QA pipeline 108 has about the evidence that the candidate answer is inferred by the input question, i.e., that the candidate answer is the correct answer for the input question.

The resulting confidence scores or measures are processed by a final confidence merging and ranking stage 370 which compares the confidence scores and measures to each other, compares them against predetermined thresholds, or performs any other analysis on the confidence scores to determine which hypotheses/candidate answers are the most likely to be the correct answer to the input question. The hypotheses/candidate answers are ranked according to these comparisons to generate a ranked listing of hypotheses/candidate answers (hereafter simply referred to as “candidate answers”). From the ranked listing of candidate answers, at stage 380, a final answer and confidence score, or final set of candidate answers and confidence scores, are generated and output to the submitter of the original input question via a graphical user interface or other mechanism for outputting information.

However, in some instances the QA pipeline 108 may not be able to answer a particular question posed in a target language due to limitation with that target language's source corpora. In that instance, the mixed-language QA supplement system 120 can interact with the QA pipeline 108 in order to support the pipeline and provide an answer that may be found in a mixed-language corpora 345 containing source material in a language different than that of the input question.

FIG. 4 depicts a block diagram illustrating the functionality of a mixed-language QA supplement system, according to embodiments described herein. First, the system can assign a function from the target language entities to the source language entities 401. The system can then assign a function from surface-level verbal types (for both the source and target languages) to one or more Abstract Universal Verbal Types 402. The system can assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language 403. The system can then assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language 404.

As described above, the mixed-language QA supplement system can receive a question Q in a target language 405. From the question Q, the system can parse at least one focus using natural language processing 406. For each focus, the system can determine if one or more verbs share a direct syntactic dependency with the focus 407. Direct syntactic dependency can mean that the verb directly depends on the subject, which in this case is the particular focus, and is determined based on the predicate and their arguments. For each of the one or more verbs that share direct syntactic dependency, the system can determine if one or more entities in the target language share direct syntactic dependency with the particular verb 408. Using the functions assigned above, the system can then determine what Abstract Universal Verbal Types are associated with each verb 409.

The system can then determine whether there exists a core dependency between a source language entity and a source language verb (both taken from a source language passage), and whether that core dependency is of the same type as the dependency between the target language verb and the target language entity (which are derived from the target language question) 410. If the core dependencies are similar, the system can return the source language entity as a member of a set 411. After repeating steps 410 and 411 for each AUVT, steps 409, 410, and 411 for each entity, steps 408, 409, 410, and 411 for each verb, and steps 407, 408, 409, 410, and 411 for each focus in the question, the system will have populated a full set of returned source language entities.

The system steps described above can also be described in algorithmic format:

 1. Let m(x) be a function from target language entities to source language entities;  2. Let a(x) be a function from surface-level verbal types (source and target language) to Abstract Universal Verbal Types;  3. Let s(x) be a function from Abstract Universal Verbal Types to verbal surface manifestations in the source language;  4. Let t(x) be a function from Abstract Universal Verbal Types to verbal surface manifestations in the target language;  5. Given a question Q, and a passage P, and an empty set R;  6. For each focus F in Q:  7. For each verb V with a direct syntactic dependency with F:  8. For each entity E with a direct syntactic dependency with V:  9. For each Abstract Universal Verbal Type A that is associated with V: 10. For each source language verb x = a(A): 11. Let e = m(E); 12. If there is a Core Dependency between e and x of the same type as the dependency between V and E: 13. Let r = the entity connected to x by the same dependency between V and the focus; 14. Add r to R 15. Return R

The functionality of the mixed-language QA supplement system up until stage 411 can be beneficially described through example. In an example, the QA pipeline may be presented with a question in a target language of Arabic, such as “

,” which, when translated, means, “Anna Harrison was buried at what location?” However, this question about the wife of the 9^(th) president of the United States cannot be answered using standard Arab sources, such as the Arabic Wikipedia. At present, the Wikipedia page in Arabic for Anna Harrison is a stub—only one sentence long. In contrast, the English Wikipedia page for Anna Harrison is longer, and contains information that answers the operant question: “Anna Harrison died on Feb. 25, 1864, at age 88, and was buried at the William Henry Harrison Tomb State Memorial in North Bend.”

In the question, there is a syntactic/semantic relation between the verb

(bury) and the named entity

(Anna Harrison). The named entity

can be mapped to “Anna Harrison” using a correspondence mapping of named entities 121 in the target language to corresponding named entities in the source language. For instance, a manifestation of the correspondence mapping of entities 121 can be the “same-as” links in Wikipedia, which can connect equivalent pages in different languages. The verb “bury” is mapped to the verbs “

.” Presented with the English source language passage above, the system can detect that the syntactic relationship between “bury” and “Anna Harrison” in the source language passage is the same as the syntactic relationship between “

” and “

” in the target language question. This tells us that this passage is relevant to the question. The system can also detect that the syntactic relation between “

” in the question and the focus is present in the passage. Following that relation in the passage can lead the system to the phrase “William Henry Harrison Tomb State Memorial.” This phrase can then be used to generate the corresponding Arabic phrase, or score it according to the passage 415.

The system may identify one or more parallel passages using the set of returned source language entities 412. A parallel passage can be a passage wherein all of the core arguments of the verb (excluding, perhaps, the focus argument) are matched. The system can then proceed to identify the presence or absence of oblique nominal arguments 413. Oblique nominal arguments can be verbal arguments that are less strongly connected to the verb, and which can manifest in looser ways, especially cross-lingually. Oblique nominal arguments can appear inside a subordinate clause, or even in an entirely different sentence, based on the grammatical norms of the target and source languages.

For example, in the question “Who was elected Prime Minister in Canada in 2008?,” the system can first establish a positive match on the verb and the single core argument (“prime minister”), and then can look more broadly for the presence or absence of equivalent terms to “Canada” and “2008” elsewhere in the passage/title. In identifying oblique nominal arguments, the system can be tolerant of weaker semantic relations, such as to account for sentences like “In the year of 2006, the people of Canada elected Stephen Harper as Prime Minister,” where the key oblique arguments (“2006” and “Canada”) are only weakly related to the verb. The system can also allow for oblique nominal arguments to be represented in the title of the source language passage (e.g., “Elections in Canada”, “People Elected in 2006”). The system can measure the precision of the oblique nominal arguments in the parallel passages against those present in the target language question 414. The system can then return an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their oblique nominal arguments 415. In an embodiment, the scoring can be based on a logarithmic scale.

As another example of the system considering oblique nominal arguments, consider the question in a target language of Spanish: “Quien fue elegido presidente en los estados unidos en el alio dos mil?” (“Who was elected President of the United States in the year 2000?”). The system, through performing the steps, may identify the following source language passages as parallel passages: 1) a passage in English stating that “George W. Bush was elected president after a lengthy supreme court battle,” with the passage title “United States Presidential Election 2000;” 2) an Arabic passage that translates to “The people elected Barack Obama President of the United States in 2008;” and 3) a Spanish passage that translates to “Clinton was elected in 1992, defeating George H.W. Bush.”

Each of the source language parallel passages satisfies the core criterion of equivalent core arguments (i.e., the verbs align to the same meaning, and the core argument (“presidente”) from the target language question is present in all three passages. Thus, the system can consider the three passages as parallel passages. The system can then ignore the core argument slot corresponding to the focus in the target language question and the several candidate answers in the passage, and can focus on the nominal modifiers. The target language question has two adjunct modifiers “Estados Unidos” and “dos mil.” The system can map the adjunct modifiers to their equivalents in the source languages of English and Arabic. The system can analyze the parallel passages and can determine that passage 1 contains both of the modifiers in the title of the document, passage 2 contains only one of the modifiers in the passage, and passage 3 contains none of the modifiers. The system can then score the parallel passages according to the presence or lack of the modifiers, with passage 1 receiving the highest score, passage 3 the lowest score, and passage 2 receiving a score between the scores of passages 1 and 3. The system can then translate and display an answer to the target language question based upon the information derived from passage 1.

The system and processes of the figures are not exclusive. Other systems, processes and menus may be derived in accordance with the principles of embodiments described herein to accomplish the same objectives. It is to be understood that the embodiments and variations shown and described herein are for illustration purposes only. Modifications to the current design may be implemented by those skilled in the art, without departing from the scope of the embodiments. As described herein, the various systems, subsystems, agents, managers, and processes can be implemented using hardware components, software components, and/or combinations thereof. No claim element herein is to be construed under the provisions of 35 U.S.C. 112, sixth paragraph, unless the element is expressly recited using the phrase “means for.”

The present description and claims may make use of the terms “a,” “at least one of,” and “one or more of,” with regard to particular features and elements of the illustrative embodiments. It should be appreciated that these terms and phrases are intended to state that there is at least one of the particular feature or element present in the particular illustrative embodiment, but that more than one can also be present. That is, these terms/phrases are not intended to limit the description or claims to a single feature/element being present or require that a plurality of such features/elements be present. To the contrary, these terms/phrases only require at least a single feature/element with the possibility of a plurality of such features/elements being within in the scope of the description and claims.

In addition, it should be appreciated that the following description uses a plurality of various examples for various elements of the illustrative embodiments to further illustrate example implementations of the illustrative embodiments and to aid in the understanding of the mechanisms of the illustrative embodiments. These examples are intended to be non-limiting and are not exhaustive of the various possibilities for implementing the mechanisms of the illustrative embodiments. It will be apparent to those of ordinary skill in the art in view of the present description that there are many other alternative implementations for these various elements that may be utilized in addition to, or in replacement of, the example provided herein without departing from the spirit and scope of the present invention.

Although the invention has been described with reference to exemplary embodiments, it is not limited thereto. Those skilled in the art will appreciate that numerous changes and modifications may be made to the preferred embodiments of the invention and that such changes and modifications may be made without departing from the true spirit of the invention. It is therefore intended that the appended claims be construed to cover all such equivalent variations as fall within the true spirit and scope of the invention. 

1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising: receiving a question in a target language; determining the question cannot be answered using a target-language only corpus; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: using a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; training a statistical model employed by a question answer pipeline; applying the trained statistical model to determine a weight for each dependency score; applying the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, returning the source language entity as a member of a set; populating the set of returned source language entities for each focus in the target language question; identifying one or more parallel passages wherein all core arguments are matched; for each parallel passage: identifying the presence or absence of oblique nominal arguments; and measuring the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and returning an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.
 2. The method as recited in claim 1, further comprising: storing the one or more Abstract Universal Verbal Types in an ontology.
 3. The method as recited in claim 1, further comprising: receiving a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 4. The method as recited in claim 3, further comprising: generating the correspondence mapping through natural language analysis of a mixed-language corpus.
 5. The method as recited in claim 1, further comprising: assigning a function from the target language entities to the source language entities.
 6. The method as recited in claim 1, further comprising: assigning a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types.
 7. The method as recited in claim 1, further comprising: assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language.
 8. The method as recited in claim 1, further comprising: assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.
 9. A computer program product for question and answer generation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: receive a question in a target language; determine the question cannot be answered using a target-language only corpus; apply natural language processing to parse the question into at least one focus; for each focus, determine if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determine if one or more target language entities share direct syntactic dependency with the verb; determine one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determine whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: use a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; train a statistical model employed by a question answer pipeline; apply the trained statistical model to determine a weight for each dependency score; apply the weight to each dependency score to generate weighted dependency scores; and process the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, return the source language entity as a member of a set; populate the set of returned source language entities for each focus in the target language question; identify one or more parallel passages wherein all core arguments are matched; for each parallel passage: identify the presence or absence of oblique nominal arguments; and measure the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and return an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.
 10. The computer program product as recited in claim 9, wherein the processor is further caused to: store the one or more Abstract Universal Verbal Types in an ontology.
 11. The computer program product as recited in claim 9, wherein the processor is further caused to: receive a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 12. The computer program product as recited in claim 11, wherein the processor is further caused to: generate the correspondence mapping through natural language analysis of a mixed-language corpus.
 13. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the target language entities to the source language entities.
 14. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types.
 15. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language.
 16. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.
 17. A mixed-language question answering supplement system, comprising: a mixed-language question answering supplement processor configured to: receive a question in a target language; determine the question cannot be answered using a target-language only corpus; apply natural language processing to parse the question into at least one focus; for each focus, determine if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determine if one or more target language entities share direct syntactic dependency with the verb; determine one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determine whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: use a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; train a statistical model employed by a question answer pipeline; apply the trained statistical model to determine a weight for each dependency score; apply the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, return the source language entity as a member of a set; populate the set of returned source language entities for each focus in the target language question; identify one or more parallel passages wherein all core arguments are matched; for each parallel passage: identify the presence or absence of oblique nominal arguments; and measure the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and return an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.
 18. The system as recited in claim 17, wherein the mixed-language question answering supplement processor is further configured to: receive a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 19. The system as recited in claim 18, wherein the mixed-language question answering supplement processor is further configured to: generate the correspondence mapping through natural language analysis of a mixed-language corpus.
 20. The system as recited in claim 17, wherein the mixed-language question answering supplement processor is further configured to: assign a function from the target language entities to the source language entities; assign a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types; assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language; and assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language. 